Given the clear impact that mobility has on economic and social development, the continuous increase in the number of vehicles coupled with the increase in mobility behaviour of people are creating new problems and challenges that need to be holistically addressed to ensure safe, sustainable, efficient and environmentally friendly mobility systems. In this context, increasing road traffic congestions can represent a serious risk for countries to lose their economic competitiveness. Therefore, there has been an increasing focus globally to use Intelligent Transport Systems (ITS) and applications to address transport and mobility issues.
As the economy is increasing the road traffic has continued to keep pace with the growing economy. More and more industries, companies, offices add more and more employees which eventually increases the vehicles on the roads. The road traffic has increased at rates greater than growth in road infrastructure capacity. Accordingly, there are more number of vehicles to be accommodated in a deficiently growing infrastructure system, thereby resulting in traffic congestion. As used herein, traffic congestion refers to a condition on road networks that occurs as use increases, and is characterized by slower speeds, longer trip times, and increased vehicular queuing. The most common example is the physical use of roads by vehicles. When traffic demand is great enough that the interaction between vehicles slows the speed of the traffic stream, this results in some congestion. As demand approaches the capacity of a road (or of the intersections along the road), extreme traffic congestion sets in. The effects of increasing traffic congestion have had growing effects on businesses, government operations and on individuals. Most of the people spend too much time getting stuck in traffic jams than the travel time to their workplace. The time spent in traffic congestion is non productive and waste of energy. Efforts have been made to tackle the increasing traffic congestion in various ways, such as by obtaining information about current traffic conditions and providing the information to individuals and organizations. By knowing the current traffic congestion about a road, an individual can make a decision to travel from another route to avoid getting stuck in the traffic. The current traffic condition information of a route may be provided to interested parties in various ways like by frequent radio broadcasts, an Internet Web site that displays a map of a geographical area with color-coded information about current traffic congestion on some major roads in the geographical area, information sent to cellular telephones and other portable consumer devices etc. Estimating the traffic congestion on roads is very useful for all users (customers, enterprise, and government agencies) since it helps in optimizing routing and reducing CO2 emissions.
Presently, one method of obtaining current traffic condition includes observations shared by fellow travelers who are travelling on a road segment of interest. They share their experience about the time taken on the road to cross a certain street. For example, a person can tell that he entered the street at 9:30 A.M. and exited around 10:15 A.M. The traffic information providing agency, for example, a FM radio channel, receives it and calculates the travel time and average speed for crossing the particular street whose length is known. The FM radio channel broadcasts the traffic information to its audience and the audience can make decision of changing routes based on their needs. While human-supplied observations may provide some value in limited situations, such information is typically limited to only a few areas at a time and typically lacks sufficient detail to be of significant use.
Another method used in some larger metropolitan cities includes networks of traffic sensors capable of measuring traffic for various roads in the area, for example, via sensors embedded in the road pavement. The sensors may be Bluetooth technology based and detect the Bluetooth device in a vehicle and report its identifier, time of detection, its class, the radio signal strength (RSSI), and the like. Such data from a plurality of sensors along the road can help in predicting the travel time on the road at a particular time.
Information about road traffic conditions may also be obtained from vehicles on the road, which include a GPS (“Global Positioning System”) device and/or other geo-location device capable of determining the geographic location, speed, direction, and/or other data that is related to the vehicle's travel. The devices on the vehicle (like GPS) or a distinct communication device may from time to time provide such data by way of a wireless link to systems able to use the data. This information can be also obtained from Road-Side Units (RSUs) that are deployed along roads and receive data from nearby On-Board Units (OBUs) using vehicular to infrastructure short range communication technologies like the 802.11p standard which will be also used for vehicle to vehicle communications.
GPS and other communication devices on the vehicle and the Bluetooth sensors on the roads may communicate with external systems that can detect and track information about vehicles (for example, vehicles passing by each of multiple receivers on the road in a network operated by the system), thus allowing location and movement information for the vehicles without interacting with devices (for example, camera systems that can observe and identify license plates and/or users' faces). Such external systems may include, for example, cellular telephone towers and networks, other wireless networks (for example, a network of Wi-Fi hotspots), detectors of vehicle transponders using various communication techniques (for example, RFID, or “Radio Frequency Identification”), other detectors of vehicles and/or users (e.g., using infrared, sonar, radar or laser ranging devices to determine location and/or speed of vehicles), and the like.
A huge amount of data from a plurality of vehicles or sensors is obtained at different timestamps. The obtained data may include multiple data samples, including data samples provided by mobile data sources (e.g., GPS devices on the vehicles), data readings from road-based traffic sensors (e.g., Bluetooth sensors embedded in road pavement), and data from other data sources. The data may be analyzed in various manners to facilitate determination of traffic condition characteristics of interest, such as estimated average travel time, traffic speed and estimated total volume of vehicles for particular segments of roads of interest, and to enable such traffic condition determinations to be performed in near-realtime manner (e.g., within a few minutes of receiving the underlying data samples).
Data obtained from multiples sources used for the calculation of average traffic time and average speed, also contains data which may be unrepresentative of actual traffic condition characteristics of interest such data obtained from road segments which are located near to the road segment of interest or the data sending sensors are not accurate etc. Such data should be filtered form the data obtained. The assessed data may then be utilized in order to perform other functions related to analyzing, predicting, forecasting, incident detection, and/or providing traffic-related information.
However, even if accurate and timely information about current traffic conditions was available from such sensors, current traffic information does not indicate future traffic conditions for roads segments of interest. Limited attempts have been made to generate and provide information about possible future traffic conditions, but such attempts have typically suffered from inaccuracies in the generated information, as well as various other problems. For example, some efforts to provide information about possible future traffic conditions have merely calculated and provided historical averages of accumulated data. While such historical averages may occasionally produce information for a particular place at a particular day and time that is temporarily similar to actual conditions, such historical averages cannot adapt to reflect specific current conditions that can greatly affect traffic, and thus the generated information can be of little practical use for planning purposes.
The features of above discussed conventional ways of gathering information about road traffic conditions are complex, costly, and inconvenient. In view of the drawbacks inherent in the conventional ways of gathering information about road traffic conditions, there exists a need of improved means for effectively computing average speed and travel time on a road segment and assessing traffic-related information, such as to determine current traffic-related information and/or predicted future traffic-related information for roads of interest.